Abstract
We developed a new computational method, Single-Cell Entropy Network (SCEN) to analyze single-cell RNA-seq data, which used the information of gene-gene associations to discover new heterogeneity of immune cells as well as identify existing cell types. Based on SCEN, we defined association-entropy (AE) for each cell and each gene through single-cell gene co-expression networks to measure the strength of association between each gene and all other genes at a single-cell resolution. Analyses of public datasets indicated that the AE of ribosomal protein genes (RP genes) varied greatly even in the same cell type of immune cells and the average AE of RP genes of immune cells in each person was significantly associated with the healthy/disease state of this person. Based on existing research and theory, we inferred that the AE of RP genes represented the heterogeneity of ribosomes and reflected the activity of immune cells. We believe SCEN can provide more biological insights into the heterogeneity and diversity of immune cells, especially the change of immune cells in the diseases.